Abstract
This paper, aims to address the ability of self-organising networks to automatically extract and correspond landmark points using only topological relations derived from competitive hebbian learning. We discuss, how the Growing Neural Gas (GNG) algorithm can be used for the automatic extraction and correspondence of nodes in a set of objects, which are then used to built statistical human brain MRI and hand gesture models.
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References
Angelopoulou, A., Psarrou, A., Rodríguez, J.G., Revett, K.R.: Automatic landmarking of 2D medical shapes using the growing neural gas network. In: Liu, Y., Jiang, T.-Z., Zhang, C. (eds.) CVBIA 2005. LNCS, vol. 3765, pp. 210–219. Springer, Heidelberg (2005)
Cheng, G., Zell, A.: Double growing neural gas for disease diagnosis. In: Proc. of Artificial Neural Networks in Medicine and Biology Conference (ANNIMAB-1), pp. 309–314 (2000)
Cootes, T.F., Taylor, C.J., Cooper, D.H., Graham, J.: Training Models of Shape from Sets of Examples. In: Proc. of the 3rd British Machine Vision Conference, pp. 9–18 (1992)
Cselényi, Z.: Mapping the dimensionality, density and topology of data: the growing adaptive neural gas. Computer Methods and Programs in Biomedicine 78(2), 141–156 (2005)
Fatemizadeh, E., Lucas, C., Soltania-Zadeh, H.: Automatic Landmark Extraction from Image Data Using Modified Growing Neural Gas Network. IEEE Transactions on Information Technology in Biomedicine 7(2), 77–85 (2003)
Fritzke, B.: Growing Cell Structures - a self-organising network for unsupervised and supervised learning. The Journal of Neural Networks 7(9), 1441–1460 (1994)
Fritzke, B.: A growing Neural Gas Network Learns Topologies. In: Advances in Neural Information Processing Systems 7 (NIPS 1994), pp. 625–632 (1995)
Kohonen, T.: Topology Representing Networks. Springer, Heidelberg (1994)
Marsland, S., Nehmzow, U., Shapiro, J.: A real-time novelty detector for a mobile robot. In: Proc. of EUREL European Advanced Robotics Systems Masterclass and Conference (2000)
Martinez, T., Ritter, H., Schulten, K.: Three dimensional neural net for learning visuomotor-condination of a robot arm. IEEE Transactions on Neural Networks 1, 131–136 (1990)
Martinez, T., Schulten, K.: Topology Representing Networks. The Journal of Neural Networks 7(3), 507–522 (1994)
Nasrabati, M., Feng, Y.: Vector Quantisation of images based upon Kohonen self-organizing feature maps. In: Proc. IEEE Int. Conf. Neural Networks., pp. 1101–1108 (1988)
Ogura, T., Iwasaki, K., Sato, C.: Topology representing network enables highly accurate classification of protein images taken by cryo electron-microscope without masking. Journal of Structural Biology 143(3), 185–200 (2003)
Ritter, H., Schulten, K.: Topology conserving mappings for learning motor tasks. In: AIP Conf. Proc. Neural Networks for Computing, pp. 376–380 (1986)
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Angelopoulou, A., Psarrou, A., García Rodríguez, J. (2011). Object Representation with Self-Organising Networks. In: Cabestany, J., Rojas, I., Joya, G. (eds) Advances in Computational Intelligence. IWANN 2011. Lecture Notes in Computer Science, vol 6692. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21498-1_31
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DOI: https://doi.org/10.1007/978-3-642-21498-1_31
Publisher Name: Springer, Berlin, Heidelberg
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